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Abstract #2949

Generating a Human Neonatal Brain Atlas for Superior Normalization Accuracy

Yajing Zhang1, Linda Chang2, Thomas Ernst2, Jon Skranes3, Steven Buchthal2, Daniel Alicata2, Heather Johansen2, Antonette Hernandez2, Robyn Yamakawa2, Lillian Fujimoto4, Michael Miller1, Susumu Mori5, Kenichi Oishi5

1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States; 2University of Hawaii at Manoa, Honolulu, HI, United States; 3Department of Laboratory Medicine, Children's & Women's Health, Norwegian Univ. of Science and Technology, Trondheim, Norway; 4Straub Mililani Clinic, Mililani, HI, United States; 5Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, United States

MRI is a sensitive method for detecting subtle anatomical abnormalities in neonatal brains. The tissue composition of the neonatal brain is substantially different from that of the adult, and therefore, the use of a neonate-specific atlas might be more appropriate. To optimize the normalization, we introduce a method to create a Bayesian neonatal brain atlas to represent the studied population. Anatomical parcellation can be obtained automatically, avoiding the labor-intensive manual drawings of 3D ROIs. This tool is expected to be applicable for whole-brain detection of subtle developmental abnormalities, and for identifying MRI-based markers of neurological disorders in neonatal brain development.